249 citations · 442 across the 7 of their papers we have counts for
7 papers
Discriminative Learning via Semidefinite Probabilistic Models
Koby Crammer, Amir Globerson
Discriminative linear models are a popular tool in machine learning. These can be generally divided into two types: The first is linear classifiers, such as support vector machines…
Convergent Propagation Algorithms via Oriented Trees
Amir Globerson, Tommi S. Jaakkola
Inference problems in graphical models are often approximated by casting them as constrained optimization problems. Message passing algorithms, such as belief propagation, have pre…
Learning the Experts for Online Sequence Prediction
Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz +1
Online sequence prediction is the problem of predicting the next element of a sequence given previous elements. This problem has been extensively studied in the context of individu…
Tightening LP Relaxations for MAP using Message Passing
David Sontag, Talya Meltzer, Amir Globerson +2
Linear Programming (LP) relaxations have become powerful tools for finding the most probable (MAP) configuration in graphical models. These relaxations can be solved efficiently us…
Convergent message passing algorithms - a unifying view
Talya Meltzer, Amir Globerson, Yair Weiss
Message-passing algorithms have emerged as powerful techniques for approximate inference in graphical models. When these algorithms converge, they can be shown to find local (or so…
Convexifying the Bethe Free Energy
Ofer Meshi, Ariel Jaimovich, Amir Globerson +1
The introduction of loopy belief propagation (LBP) revitalized the application of graphical models in many domains. Many recent works present improvements on the basic LBP algorith…